Title: Predicting Pathologic Complete Response to neoadjuvant chemotherapy in breast cancer using Sparse Logistic Regression

Authors: Wei Hu

Addresses: Department of Computer Science, Houghton College, Houghton 14744, NY, USA

Abstract: We utilised Sparse Logistic Regression (SLR) to build two sparse and interpretable predictors. The first one (SLR-65) was based on a signature consisting of the top 65 probe sets (59 genes) differentially expressed between Pathologic Complete Response (PCR) and Residual Disease (RD) cases, and the second one (SLR-Notch) was based on the genes involved in the Notch singling related pathways (113 genes). The two predictors produced better predictions than the predictor in a previous study. The SLR-65 selected 16 informative genes and the SLR-Notch selected 12 informative genes.

Keywords: breast cancer; neoadjuvant chemotherapy; gene signature; notch signalling pathways; predictors; random forest; SLR; sparse logistic regression; pathological complete response; residual disease; informative genes; bioinformatics.

DOI: 10.1504/IJBRA.2013.053605

International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.3, pp.242 - 260

Received: 06 Aug 2010
Accepted: 04 Aug 2011
Published online: 06 Sep 2014 *

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